Rename use_mkldnn to use_onednn in test cases - part11

test/mkldnn/test_concat_bf16_mkldnn_op.py

@@ -27,16 +27,16 @@
 class TestConcatBf16Op(OpTest):
  def setUp(self):
  self.op_type = "concat"
- self.use_mkldnn = True
- self.mkldnn_data_type = "bfloat16"
+ self.use_onednn = True
+ self.onednn_data_type = "bfloat16"
  self.init_axis()
  self.init_shape()
  self.init_test_data()
  self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
  self.attrs = {
  'axis': self.axis,
  'use_mkldnn': True,
- 'mkldnn_data_type': self.mkldnn_data_type,
+ 'mkldnn_data_type': self.onednn_data_type,
  }
 
  self.sections = [self.x0.shape[self.axis]] * 2

test/mkldnn/test_concat_int8_mkldnn_op.py

@@ -21,7 +21,7 @@
 class TestConcatOp(OpTest):
  def setUp(self):
  self.op_type = "concat"
- self.use_mkldnn = True
+ self.use_onednn = True
  self._cpu_only = True
  self.init_axis()
  self.init_shape()

test/mkldnn/test_conv2d_bf16_mkldnn_op.py

@@ -37,11 +37,11 @@ def setUp(self):
  self.use_cudnn = False
  self.exhaustive_search = False
  self.use_cuda = False
- self.use_mkldnn = True
+ self.use_onednn = True
  self._cpu_only = True
  self.weight_type = np.float32
  self.input_type = np.float32
- self.mkldnn_data_type = "bfloat16"
+ self.onednn_data_type = "bfloat16"
  self.force_fp32_output = False
  self.init_group()
  self.init_dilation()
@@ -110,8 +110,8 @@ def setUp(self):
  'groups': self.groups,
  'dilations': self.dilations,
  'use_cudnn': self.use_cudnn,
- 'use_mkldnn': self.use_mkldnn,
- 'mkldnn_data_type': self.mkldnn_data_type,
+ 'use_mkldnn': self.use_onednn,
+ 'mkldnn_data_type': self.onednn_data_type,
  'force_fp32_output': self.force_fp32_output,
  'fuse_residual_connection': self.fuse_residual,
  }

test/mkldnn/test_conv2d_int8_mkldnn_op.py

@@ -36,11 +36,11 @@ def setUp(self):
  self.use_cudnn = False
  self.exhaustive_search = False
  self.use_cuda = False
- self.use_mkldnn = False
+ self.use_onednn = False
  self.data_format = "NCHW"
- self.mkldnn_data_type = "int8"
+ self.onednn_data_type = "int8"
  self.weighttype = np.float32
- self.use_mkldnn = True
+ self.use_onednn = True
  self.init_weight_quantization_type()
  self.init_group()
  self.init_dilation()
@@ -166,7 +166,7 @@ def residual_helper(init_low, init_high, output_):
  'groups': self.groups,
  'dilations': self.dilations,
  'use_cudnn': self.use_cudnn,
- 'use_mkldnn': self.use_mkldnn,
+ 'use_mkldnn': self.use_onednn,
  'data_format': self.data_format,
  'exhaustive_search': self.exhaustive_search,
  'Scale_in': self.scale_in,
@@ -177,7 +177,7 @@ def residual_helper(init_low, init_high, output_):
  'fuse_alpha': self.fuse_alpha,
  'fuse_beta': self.fuse_beta,
  'fuse_residual_connection': self.fuse_residual,
- 'mkldnn_data_type': self.mkldnn_data_type,
+ 'mkldnn_data_type': self.onednn_data_type,
  }
  self.outputs = {'Output': output}
 
@@ -471,7 +471,7 @@ def init_data_type(self):
 
 class TestConv2DOp_AsyPadding_INT_ONEDNN(TestConv2DInt8Op):
  def init_kernel_type(self):
- self.use_mkldnn = True
+ self.use_onednn = True
 
  def init_paddings(self):
  self.pad = [0, 0, 1, 2]